Predicting fraudulent financial reporting using artificial neural network

Normah Omar, Zulaikha ‘Amirah Johari and Malcolm Smith

Journal of Financial Crime, 2017, vol. 24, issue 2, 362-387

Abstract: Purpose - This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia. Design/methodology/approach - Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN. Findings - The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting. Originality/value - The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.

Keywords: ANN; Fraud prediction models; Small market capitalization companies (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:jfcpps:jfc-11-2015-0061

DOI: 10.1108/JFC-11-2015-0061

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